GPT-5.6 Terra vs Bonsai 4B

At a Glance

Compare
Bonsai 4BPrismML
Intelligence, Cost, and Efficiency
IntelligenceHigher is better · MM Intelligence v2.5#13 of 4674.9 score · 3/3 sources · completeUnrankedNot in the 46-model eligible cohort
CostLower is better · Published-token output estimate#35 of 44$0.266 per LiveBench caseUnrankedNot in the 44-model eligible cohort
EfficiencyHigher is better · MM Efficiency v1.5#23 of 3851.5 score · 3/3 sources · completeUnrankedNot in the 38-model eligible cohort
Pricing and Limits
Input priceFrom · USD / 1M tokens$2.00Openai · Sep 3, 2026Not reported
Output priceFrom · USD / 1M tokens$12.00Openai · Sep 3, 2026Not reported
Context windowMaximum documented tokens1,050K33K
Model facts checkedAug 28, 2026View model evidence →Sep 18, 2026View model evidence →

Token prices are the lowest available sourced USD rates; input and output may use different providers. Cost ranking estimates output spend on LiveBench, not a full request bill. Ranking methodology →

Available Benchmarks

All benchmark results →
No Protocol-Matched Benchmark Yet.Results appear here only when both models share the same benchmark version, metric, evaluation protocol, and evidence class.

Side-by-Side Facts

FieldGPT-5.6 TerraBonsai 4B
DeveloperOpenAIPrismML
FamilyGpt 5 6Bonsai 4b
ModelGPT-5.6 TerraBonsai 4B
VersionGPT-5.6 TerraBonsai 4B
Lifecycleactiveactive
ReleasedUnknown2026-03-29
Knowledge cutoff2026-02-16Unknown
Input modalitiesText, ImageText
Output modalitiesTextText
Context window1,050K33K
Total parametersUnknown4B
Active parametersUnknownUnknown
LicenseUnknownapache-2.0
Open weightsNoYes
API availableYesNo
Self-hostableNoYes
Provider accessOpenai (Standard), Openrouter (Standard)Unknown
Capabilitieschat, generation, reasoning, toolschat, generation
Effective bit widthUnknown1 bit per weight
Weight sizeUnknown0.57 GB
Weight formatUnknownBinary Q1_0

GPT-5.6 Terra Capabilities

chatgenerationreasoningtools
Serving providers2
Canonical IDopenai/gpt-5.6-terra

Bonsai 4B Capabilities

chatgeneration
Serving providers0
Canonical IDprism-ml/Bonsai-4B

Primary Evidence

Sources and Freshness

Questions

GPT-5.6 Terra vs Bonsai 4B FAQs

Is GPT-5.6 Terra or Bonsai 4B better for coding?+

This comparison does not currently contain a protocol-matched coding benchmark for both GPT-5.6 Terra and Bonsai 4B, so Model Markets cannot name a coding leader from pricing, context size, or capability labels alone.

Which is cheaper, GPT-5.6 Terra or Bonsai 4B?+

Only GPT-5.6 Terra has a directly sourced input price: $2.00 per million tokens. Only GPT-5.6 Terra has a directly sourced output price: $12.00 per million tokens.

Which has a larger context window, GPT-5.6 Terra or Bonsai 4B?+

GPT-5.6 Terra has the larger sourced context window. GPT-5.6 Terra supports 1,050K and Bonsai 4B supports 33K.

Which performs better in benchmarks, GPT-5.6 Terra or Bonsai 4B?+

There is no overall benchmark winner: At least two independently verified, protocol-matched benchmarks are required for an overall winner.

Can GPT-5.6 Terra or Bonsai 4B be self-hosted?+

Bonsai 4B is the only model in this pair currently marked as self-hostable. GPT-5.6 Terra is not marked open weight; Bonsai 4B is open weight.

Can GPT-5.6 Terra and Bonsai 4B understand images?+

GPT-5.6 Terra is documented with image input; Bonsai 4B is not documented with image input. This reflects supported input modalities, not vision quality.

Which can generate longer answers, GPT-5.6 Terra or Bonsai 4B?+

Neither has a larger sourced maximum output. GPT-5.6 Terra is 128K and Bonsai 4B is —.

Do GPT-5.6 Terra and Bonsai 4B support reasoning and tool use?+

GPT-5.6 Terra: reasoning, tool calling, and image input. Bonsai 4B: none of these features are definitively sourced. Feature support does not establish relative quality.

Which is available from more inference providers, GPT-5.6 Terra or Bonsai 4B?+

GPT-5.6 Terra has 2 sourced provider routes; Bonsai 4B has 0, so GPT-5.6 Terra has broader tracked availability.

Which offers better value, GPT-5.6 Terra or Bonsai 4B?+

There is no universal value winner. Compare the input and output prices above with the matched benchmark result for your workload: cheaper tokens can be offset by different quality, token usage, latency, or provider availability.

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